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Graph Neural Networks (GNNs) have become widely used in the field of graphmining. However, these networks are vulnerable to structural perturbations.While many research efforts have focused on analyzing vulnerability throughpoisoning attacks, we have identified an inefficiency in current attack losses.These losses steer the attack strategy towards modifying edges targetingmisclassified nodes or resilient nodes, resulting in a waste of structuraladversarial perturbation. To address this issue, we propose a novel attack lossframework called the Cost Aware Poisoning Attack (CA-attack) to improve theallocation of the attack budget by dynamically considering the classificationmargins of nodes. Specifically, it prioritizes nodes with smaller positivemargins while postponing nodes with negative margins. Our experimentsdemonstrate that the proposed CA-attack significantly enhances existing attackstrategies

Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,